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概要と使いどころ
Mergt één pull request of een reeks achter elkaar op main, met bijwerken zonder handtekeningverlies, wachten op de juiste checks, en controleren dat de uitrol geslaagd is. Gebruik dit bij "merge PR N", "laat de trein rijden" of het wegwerken van een stapel mergebare PR's.
日本語の概要は準備中です。原文の説明を表示しています。
Toolkit for creating animated GIFs optimized for Slack, with validators for size constraints and composable animation primitives. This skill applies when users request animated GIFs or emoji animations for Slack from descriptions like "make me a GIF for Slack of X doing Y".
日本語の概要は準備中です。原文の説明を表示しています。
Analyze Kanban flow metrics to identify bottlenecks and improve team throughput. Covers Cycle Time, Throughput, WIP, and Flow Efficiency using Little's Law and Theory of Constraints. Use when a team wants to optimize delivery flow or improve predictability. Do not use for sprint-based Scrum planning — use sprint-planning-assistant instead.
日本語の概要は準備中です。原文の説明を表示しています。
Build change management plans using ADKAR and Kotter's 8-Step framework. Covers stakeholder readiness, communication plans, training schedules, resistance management, and adoption metrics. Use when rolling out new systems, reorganizations, or process changes that affect people.
日本語の概要は準備中です。原文の説明を表示しています。
Prompt engineering and task-specification skill for improving prompts, agent instructions, system messages, evaluation prompts, coding-agent tasks, multi-step workflows, and LLM output reliability. Use when rewriting vague prompts, adding constraints, reducing ambiguity, improving safety, creating reusable prompt templates, or converting goals into actionable agent instructions.
日本語の概要は準備中です。原文の説明を表示しています。
Identify and exploit vulnerabilities in the AI Supply Chain by injecting malicious models, datasets, or dependencies. Use this skill to simulate advanced persistent threats (APTs) compromising Hugging Face repositories, manipulating pre-trained weights (Model Poisoning), and exploiting insecure deserialization during model loading (e.g., Pickle files).
日本語の概要は準備中です。原文の説明を表示しています。
Execute advanced LLM Jailbreaking techniques using roleplay, nested environments (virtual machines), and complex personas to completely bypass safety constraints and ethical alignments embedded in AI models.
日本語の概要は準備中です。原文の説明を表示しています。
Simulate supply chain and adversarial machine learning attacks by injecting poisoned data or targeted backdoors into training and fine-tuning datasets. Use this skill when assessing the integrity controls of MLOps pipelines or evaluating the resilience of AI models against highly targeted, stealthy manipulation intended to alter model behavior on specific triggers.
日本語の概要は準備中です。原文の説明を表示しています。
Generates NeMo Guardrails Colang (.co) files and YAML config blocks from a plain-language description of a chatbot's purpose, allowed behaviors, and constraints. Use this skill whenever a user wants to build guardrails for a chatbot, define allowed intents for an LLM, create an AI firewall with NeMo Guardrails, generate Colang flow definitions, or configure a semantic allow-list for a bot. Trigger this skill even when the user just describes what their bot should and shouldn't do — generating the Colang and YAML is almost always what they need next.
日本語の概要は準備中です。原文の説明を表示しています。
Identify and simulate Data Poisoning attacks aimed at degrading or skewing an AI model's accuracy. This skill focuses on Adversarial Machine Learning concepts where attackers inject malicious or mislabelled data points into training or fine-tuning datasets (e.g., feedback loops) to bias the AI.
日本語の概要は準備中です。原文の説明を表示しています。
Prevent common AI report pitfalls — bug blending, inflated threat models, and generic language. Train Claude with your best past reports for concise, technical submissions. Based on Critical Thinking Bug Bounty Podcast Episode 166.
日本語の概要は準備中です。原文の説明を表示しています。
Execute and analyze AI Data Poisoning attacks. By subtly injecting malicious or targeted misinformation into an LLM's training or fine-tuning dataset, an attacker can covertly manipulate the model's future outputs, implant backdoors, or enforce biases without altering the model architecture.
日本語の概要は準備中です。原文の説明を表示しています。
Upgrade Flutter and package dependencies in this repository while keeping .fvmrc, pubspec.yaml, code generation, CI, and tests aligned. Use when bumping Flutter, Dart constraints, direct dependencies, or generator toolchains.
日本語の概要は準備中です。原文の説明を表示しています。
Diagnose and fix Flutter layout, overflow, unbounded constraint, scroll, and responsive rendering issues in this repository while preserving the template's existing feature, theme, localization, and shared widget patterns. Use when UI overflows, text clips, widgets disappear, layouts fail on mobile/tablet/desktop, or a feature needs responsive behavior.
日本語の概要は準備中です。原文の説明を表示しています。
Use when developing OpenRCT2 plugins in TypeScript - plugin registration and game actions, window and widget UI, event hooks, park, map, ride and entity access, network API, storage, or ES5 runtime constraints
日本語の概要は準備中です。原文の説明を表示しています。
Use when validating and serializing Python data with Pydantic v2 - models, field and model validators, constrained and special types, TypeAdapter, discriminated unions, BaseSettings, or FastAPI integration
日本語の概要は準備中です。原文の説明を表示しています。
Redesign the resume and cover letter templates into your own house style. Changes typography, palette, and layout while holding the ATS constraints fixed, then proves the result still compiles and still extracts. Run any time; not a pipeline stage.
日本語の概要は準備中です。原文の説明を表示しています。
Frappe LMS customization guidance for courses, batches, lessons, quizzes, assessments, enrollment, progress, certificates, portals, and learning workflows. Use when work touches Frappe LMS or education/training flows.
日本語の概要は準備中です。原文の説明を表示しています。
Backend specification planning frameworks. Use when a spec touches API endpoints, database schema, middleware, or auth. Covers endpoint contracts with request/response shapes, error catalogs, auth per endpoint, schema design with constraints and indexes, migration strategy, and middleware pipeline ordering.
日本語の概要は準備中です。原文の説明を表示しています。
Replicate SDK patterns for TypeScript/Node.js -- client setup, predictions, streaming, webhooks, file handling, model versioning, deployments, and training
日本語の概要は準備中です。原文の説明を表示しています。
Knowledge and utilities for creating animated GIFs optimized for Slack. Provides constraints, validation tools, and animation concepts. Use when users request animated GIFs for Slack like "make me a GIF of X doing Y for Slack."
日本語の概要は準備中です。原文の説明を表示しています。
General workflow for auditing ML CI failures, experiment regressions, training run failures, golden metric failures, and telemetry-backed ML work-product claims from local repositories, logs, metrics, configs, and artifacts. Use when Codex needs to decide whether an ML failure is a model/convergence issue, correctness bug, data/config issue, infrastructure/runtime issue, evaluation/gating policy issue, or unsupported claim, and produce structured evidence-backed outputs.
日本語の概要は準備中です。原文の説明を表示しています。
Full-featured Kling 3.0 Omni video generation skill. Covers text-to-video, image-to-video, video editing (base mode), video reference (feature mode), multi-shot generation, and audio-synced video. Includes validated API constraint rules and prompt engineering guide.
日本語の概要は準備中です。原文の説明を表示しています。
Revise an already accepted article, essay, paper, statement, or manuscript under strict minimal-change constraints while still satisfying reviewer comments, supervisor feedback, or requested edits. Use when the user asks for minimal edits, smallest necessary changes, preserve the current voice, keep the existing style, avoid broad rewriting, or implement requested revisions without unnecessary polishing.
日本語の概要は準備中です。原文の説明を表示しています。